Book Description
Explore the multidisciplinary nature of complex networks through machine learning techniques
Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks.
Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include:
A survey of computational approaches to reconstruct and partition biological networks
An introduction to complex networks—measures, statistical properties, and models
Modeling for evolving biological networks
The structure of an evolving random bipartite graph
Densitybased enumeration in structured data
Hyponym extraction employing a weighted graph kernel
Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduatelevel, crossdisciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.
Table of Contents
 Cover
 Title Page
 Copyright
 Dedication
 Preface
 Contributors
 Chapter 1: A Survey of Computational Approaches to Reconstruct and Partition Biological Networks

Chapter 2: Introduction to Complex Networks: Measures, Statistical Properties, and Models
 2.1 Introduction
 2.2 Representation of Networks
 2.3 Classical Network
 2.4 ScaleFree Network
 2.5 SmallWorld Network
 2.6 Clustered Network
 2.7 Hierarchical Modularity
 2.8 Network Motif
 2.9 Assortativity
 2.10 Reciprocity
 2.11 Weighted Networks
 2.12 Network Complexity
 2.13 Centrality
 2.14 Conclusion
 References

Chapter 3: Modeling for Evolving Biological Networks
 3.1 Introduction
 3.2 Unified Evolving Network Model: Reproduction of Heterogeneous Connectivity, Hierarchical Modularity, and Disassortativity
 3.3 Modeling Without Parameter Tuning: A Case Study of Metabolic Networks
 3.4 Bipartite Relationship: A Case Study of Metabolite Distribution
 3.5 Conclusion
 References
 Chapter 4: Modularity Configurations in Biological Networks with Embedded Dynamics
 Chapter 5: Influence of Statistical Estimators on the LargeScale Causal Inference of Regulatory Networks

Chapter 6: Weighted Spectral Distribution: A Metric for Structural Analysis of Networks
 6.1 Introduction
 6.2 Weighted Spectral Distribution
 6.3 A Simple Worked Example
 6.4 The Internet Autonomous System Topology
 6.5 Comparing Topology Generators
 6.6 Tuning Topology Generator Parameters
 6.7 Generating Topologies with Optimum Parameters
 6.8 Internet Topology Evolution
 6.9 Conclusions
 References
 Chapter 7: The Structure of an Evolving Random Bipartite Graph

Chapter 8: Graph Kernels
 8.1 Introduction
 8.2 Convolution Kernels
 8.3 Random Walk Graph Kernels
 8.4 PathBased Graph Kernels
 8.5 TreePattern Graph Kernels
 8.6 Cyclic Pattern Kernels
 8.7 Graphlet Kernels
 8.8 Optimal Assignment Kernels
 8.9 Other Graph Kernels
 8.10 Applications in Bioand Cheminformatics
 8.11 Summary and Conclusions
 Acknowledgments
 References
 Chapter 9: NetworkBased Information Synergy Analysis for Alzheimer Disease
 Chapter 10: DensityBased Set Enumeration in Structured Data

Chapter 11: Hyponym Extraction Employing a Weighted Graph Kernel
 11.1 Introduction
 11.2 Related Work
 11.3 Drawbacks of Current Approaches
 11.4 Semantic Networks Following the MultiNet Formalism
 11.5 Support Vector Machines and Kernels
 11.6 Architecture
 11.7 Graph Kernel
 11.8 Graph Kernel Extensions
 11.9 Distance Weighting
 11.10 Features for Hyponymy Extraction
 11.11 Evaluation
 11.12 Conclusion and Outlook
 Acknowledgments
 References
 Index
Product Information
 Title: Statistical and Machine Learning Approaches for Network Analysis
 Author(s):
 Release date: August 2012
 Publisher(s): Wiley
 ISBN: 9780470195154